Super-resolution (SR) refers to a process of recovering the missing high-frequency details of a given low-resolution (LR) image. In other words, SR produces a high-resolution (HR) image with clear and detailed contents using one or more LR observations. One class of SR processes relates to Single Image Super-Resolution.
Various image up-sampling techniques are known, including some that use a single low-resolution image and some that are not using any external information or database. Some SR techniques use upsampling of spatial texture patches for giving local geometric similarities of LR and HR image patch spaces.
The following is an outline of how this problem is currently addressed. Classical SR methods, e.g. [1]-[3], try to fuse a set of LR images in order to recover the unknown HR image. These algorithms assume that the missing high-frequency information is distributed implicitly along the LR observations, and the HR image can be recovered successfully if there is enough number of LR images. The quality of the reconstructed HR image therefore depends highly on the amount of data available in the LR images.
However, in practice, insufficient number of LR observations, registration (i.e. motion estimation) errors, and unknown point spread function (PSF) limit the applicability of these multi-image SR methods to small upscaling ratios, which is less than 2 under general conditions [4].
Example-based methods have been proposed in order to overcome the limitations of classical multi-image SR. In [5], LR and HR image patch pairs are collected from other natural images, and low- and high-frequency relations of these patches are learned via a Markov network using belief propagation. This method has later been simplified in [6] to give a fast and approximate solution to the Markov network, with a sequence of predictions of HR patches by a nearest neighbor (NN) search from the database of the collected training examples. The missing high-frequency details are estimated (“hallucinated”) according to the local LR image information and the high-frequency patch compatibilities of the recently recovered part of the HR image. Similar NN-based approaches have largely been exploited in the context of example-based texture synthesis [7]-[9], and have been shown to be beneficial in different image processing applications, e.g. in [10]-[12].
Nevertheless, one is required here to construct databases of enormous numbers of training LR and HR patch pairs in order to be representative enough for SR, and thus, this is computationally intractable or unusable for most of the practical applications.